Overview
Brought to you by YData
Dataset statistics
| Number of variables | 23 |
|---|---|
| Number of observations | 102 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 0 |
| Duplicate rows (%) | 0.0% |
| Total size in memory | 108.6 KiB |
| Average record size in memory | 1.1 KiB |
Variable types
| Numeric | 20 |
|---|---|
| Text | 2 |
| Categorical | 1 |
Engagement_Rate is highly overall correlated with Home_ratio and 1 other fields | High correlation |
Explore_ratio is highly overall correlated with From Explore and 2 other fields | High correlation |
Follows is highly overall correlated with From Explore and 7 other fields | High correlation |
From Explore is highly overall correlated with Explore_ratio and 6 other fields | High correlation |
From Hashtags is highly overall correlated with Follows and 6 other fields | High correlation |
From Home is highly overall correlated with Impressions and 4 other fields | High correlation |
From Other is highly overall correlated with Follows and 2 other fields | High correlation |
Hashtag_ratio is highly overall correlated with From Hashtags and 2 other fields | High correlation |
Home_ratio is highly overall correlated with Engagement_Rate and 8 other fields | High correlation |
Impressions is highly overall correlated with Follows and 8 other fields | High correlation |
Likes is highly overall correlated with Follows and 8 other fields | High correlation |
Other_ratio is highly overall correlated with From Other | High correlation |
Post_ID is highly overall correlated with Follows and 2 other fields | High correlation |
Profile Visits is highly overall correlated with Follows and 4 other fields | High correlation |
Saves is highly overall correlated with From Explore and 5 other fields | High correlation |
Shares is highly overall correlated with From Home and 3 other fields | High correlation |
Top_source is highly overall correlated with Explore_ratio and 6 other fields | High correlation |
saves_to_likes is highly overall correlated with Engagement_Rate and 6 other fields | High correlation |
Post_ID is uniformly distributed | Uniform |
Post_ID has unique values | Unique |
Engagement_Rate has unique values | Unique |
Home_ratio has unique values | Unique |
Explore_ratio has unique values | Unique |
Hashtag_ratio has unique values | Unique |
Other_ratio has unique values | Unique |
saves_to_likes has unique values | Unique |
Comments has 3 (2.9%) zeros | Zeros |
Shares has 5 (4.9%) zeros | Zeros |
Follows has 8 (7.8%) zeros | Zeros |
Reproduction
| Analysis started | 2025-09-13 11:15:38.733873 |
|---|---|
| Analysis finished | 2025-09-13 11:16:35.818444 |
| Duration | 57.08 seconds |
| Software version | ydata-profiling vv4.16.1 |
| Download configuration | config.json |
Variables
Post_ID
Real number (ℝ)
High correlation Uniform Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 51.5 |
| Minimum | 1 |
|---|---|
| Maximum | 102 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 1 |
|---|---|
| 5-th percentile | 6.05 |
| Q1 | 26.25 |
| median | 51.5 |
| Q3 | 76.75 |
| 95-th percentile | 96.95 |
| Maximum | 102 |
| Range | 101 |
| Interquartile range (IQR) | 50.5 |
Descriptive statistics
| Standard deviation | 29.588849 |
|---|---|
| Coefficient of variation (CV) | 0.57454076 |
| Kurtosis | -1.2 |
| Mean | 51.5 |
| Median Absolute Deviation (MAD) | 25.5 |
| Skewness | 0 |
| Sum | 5253 |
| Variance | 875.5 |
| Monotonicity | Strictly increasing |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1 | 1 | 1.0% |
| 2 | 1 | 1.0% |
| 3 | 1 | 1.0% |
| 4 | 1 | 1.0% |
| 5 | 1 | 1.0% |
| 6 | 1 | 1.0% |
| 7 | 1 | 1.0% |
| 8 | 1 | 1.0% |
| 9 | 1 | 1.0% |
| 10 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 1 | |
| 5 | 1 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 1 | |
| 10 | 1 |
| Value | Count | Frequency (%) |
| 102 | 1 | |
| 101 | 1 | |
| 100 | 1 | |
| 99 | 1 | |
| 98 | 1 | |
| 97 | 1 | |
| 96 | 1 | |
| 95 | 1 | |
| 94 | 1 | |
| 93 | 1 |
Impressions
Real number (ℝ)
High correlation
| Distinct | 101 |
|---|---|
| Distinct (%) | 99.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 5920.2549 |
| Minimum | 1941 |
|---|---|
| Maximum | 36919 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 1941 |
|---|---|
| 5-th percentile | 2412.55 |
| Q1 | 3556 |
| median | 4343.5 |
| Q3 | 6296.25 |
| 95-th percentile | 13572.45 |
| Maximum | 36919 |
| Range | 34978 |
| Interquartile range (IQR) | 2740.25 |
Descriptive statistics
| Standard deviation | 5139.8881 |
|---|---|
| Coefficient of variation (CV) | 0.86818696 |
| Kurtosis | 19.417842 |
| Mean | 5920.2549 |
| Median Absolute Deviation (MAD) | 1103.5 |
| Skewness | 3.9777248 |
| Sum | 603866 |
| Variance | 26418450 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 5394 | 2 | 2.0% |
| 3920 | 1 | 1.0% |
| 4021 | 1 | 1.0% |
| 4528 | 1 | 1.0% |
| 2518 | 1 | 1.0% |
| 3884 | 1 | 1.0% |
| 2621 | 1 | 1.0% |
| 3541 | 1 | 1.0% |
| 3749 | 1 | 1.0% |
| 4115 | 1 | 1.0% |
| Other values (91) | 91 |
| Value | Count | Frequency (%) |
| 1941 | 1 | |
| 2064 | 1 | |
| 2191 | 1 | |
| 2218 | 1 | |
| 2327 | 1 | |
| 2407 | 1 | |
| 2518 | 1 | |
| 2523 | 1 | |
| 2621 | 1 | |
| 2766 | 1 |
| Value | Count | Frequency (%) |
| 36919 | 1 | |
| 32695 | 1 | |
| 17713 | 1 | |
| 17396 | 1 | |
| 16062 | 1 | |
| 13700 | 1 | |
| 11149 | 1 | |
| 11068 | 1 | |
| 10933 | 1 | |
| 10667 | 1 |
From Home
Real number (ℝ)
High correlation
| Distinct | 97 |
|---|---|
| Distinct (%) | 95.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2496.9118 |
| Minimum | 1133 |
|---|---|
| Maximum | 13473 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 1133 |
|---|---|
| 5-th percentile | 1344.4 |
| Q1 | 1923.75 |
| median | 2216 |
| Q3 | 2605.25 |
| 95-th percentile | 3808.2 |
| Maximum | 13473 |
| Range | 12340 |
| Interquartile range (IQR) | 681.5 |
Descriptive statistics
| Standard deviation | 1588.3774 |
|---|---|
| Coefficient of variation (CV) | 0.63613677 |
| Kurtosis | 33.484056 |
| Mean | 2496.9118 |
| Median Absolute Deviation (MAD) | 365.5 |
| Skewness | 5.4045063 |
| Sum | 254685 |
| Variance | 2522942.8 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1975 | 3 | 2.9% |
| 2415 | 2 | 2.0% |
| 3152 | 2 | 2.0% |
| 2125 | 2 | 2.0% |
| 2586 | 1 | 1.0% |
| 2046 | 1 | 1.0% |
| 1543 | 1 | 1.0% |
| 2727 | 1 | 1.0% |
| 2085 | 1 | 1.0% |
| 2609 | 1 | 1.0% |
| Other values (87) | 87 |
| Value | Count | Frequency (%) |
| 1133 | 1 | |
| 1179 | 1 | |
| 1304 | 1 | |
| 1308 | 1 | |
| 1323 | 1 | |
| 1338 | 1 | |
| 1466 | 1 | |
| 1502 | 1 | |
| 1543 | 1 | |
| 1570 | 1 |
| Value | Count | Frequency (%) |
| 13473 | 1 | |
| 11815 | 1 | |
| 5185 | 1 | |
| 4439 | 1 | |
| 4137 | 1 | |
| 3813 | 1 | |
| 3717 | 1 | |
| 3401 | 1 | |
| 3152 | 2 | |
| 3144 | 1 |
From Hashtags
Real number (ℝ)
High correlation
| Distinct | 100 |
|---|---|
| Distinct (%) | 98.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1968.2843 |
| Minimum | 116 |
|---|---|
| Maximum | 11817 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 116 |
|---|---|
| 5-th percentile | 214.15 |
| Q1 | 753 |
| median | 1326 |
| Q3 | 2415.75 |
| 95-th percentile | 5761.8 |
| Maximum | 11817 |
| Range | 11701 |
| Interquartile range (IQR) | 1662.75 |
Descriptive statistics
| Standard deviation | 1977.2981 |
|---|---|
| Coefficient of variation (CV) | 1.0045795 |
| Kurtosis | 8.1060627 |
| Mean | 1968.2843 |
| Median Absolute Deviation (MAD) | 718.5 |
| Skewness | 2.504723 |
| Sum | 200765 |
| Variance | 3909707.9 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 362 | 2 | 2.0% |
| 411 | 2 | 2.0% |
| 1028 | 1 | 1.0% |
| 1838 | 1 | 1.0% |
| 621 | 1 | 1.0% |
| 1188 | 1 | 1.0% |
| 599 | 1 | 1.0% |
| 255 | 1 | 1.0% |
| 628 | 1 | 1.0% |
| 857 | 1 | 1.0% |
| Other values (90) | 90 |
| Value | Count | Frequency (%) |
| 116 | 1 | |
| 139 | 1 | |
| 166 | 1 | |
| 183 | 1 | |
| 201 | 1 | |
| 212 | 1 | |
| 255 | 1 | |
| 278 | 1 | |
| 349 | 1 | |
| 362 | 2 |
| Value | Count | Frequency (%) |
| 11817 | 1 | |
| 10008 | 1 | |
| 7761 | 1 | |
| 6610 | 1 | |
| 6564 | 1 | |
| 5799 | 1 | |
| 5055 | 1 | |
| 4604 | 1 | |
| 4221 | 1 | |
| 4176 | 1 |
From Explore
Real number (ℝ)
High correlation
| Distinct | 95 |
|---|---|
| Distinct (%) | 93.1% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1178.5686 |
| Minimum | 0 |
|---|---|
| Maximum | 17414 |
| Zeros | 1 |
| Zeros (%) | 1.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 45.15 |
| Q1 | 178.75 |
| median | 337 |
| Q3 | 728.5 |
| 95-th percentile | 5619.9 |
| Maximum | 17414 |
| Range | 17414 |
| Interquartile range (IQR) | 549.75 |
Descriptive statistics
| Standard deviation | 2797.2126 |
|---|---|
| Coefficient of variation (CV) | 2.3733982 |
| Kurtosis | 21.208058 |
| Mean | 1178.5686 |
| Median Absolute Deviation (MAD) | 223.5 |
| Skewness | 4.4403695 |
| Sum | 120214 |
| Variance | 7824398.4 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 248 | 2 | 2.0% |
| 45 | 2 | 2.0% |
| 48 | 2 | 2.0% |
| 84 | 2 | 2.0% |
| 360 | 2 | 2.0% |
| 182 | 2 | 2.0% |
| 468 | 2 | 2.0% |
| 333 | 1 | 1.0% |
| 500 | 1 | 1.0% |
| 178 | 1 | 1.0% |
| Other values (85) | 85 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 29 | 1 | |
| 36 | 1 | |
| 37 | 1 | |
| 45 | 2 | |
| 48 | 2 | |
| 51 | 1 | |
| 59 | 1 | |
| 60 | 1 | |
| 69 | 1 |
| Value | Count | Frequency (%) |
| 17414 | 1 | |
| 16444 | 1 | |
| 12389 | 1 | |
| 6000 | 1 | |
| 5762 | 1 | |
| 5634 | 1 | |
| 5352 | 1 | |
| 5192 | 1 | |
| 2355 | 1 | |
| 2266 | 1 |
From Other
Real number (ℝ)
High correlation
| Distinct | 84 |
|---|---|
| Distinct (%) | 82.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 184.54902 |
| Minimum | 9 |
|---|---|
| Maximum | 2547 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 9 |
|---|---|
| 5-th percentile | 21.1 |
| Q1 | 40.25 |
| median | 75 |
| Q3 | 218.5 |
| 95-th percentile | 650.3 |
| Maximum | 2547 |
| Range | 2538 |
| Interquartile range (IQR) | 178.25 |
Descriptive statistics
| Standard deviation | 309.09605 |
|---|---|
| Coefficient of variation (CV) | 1.6748724 |
| Kurtosis | 34.227684 |
| Mean | 184.54902 |
| Median Absolute Deviation (MAD) | 47.5 |
| Skewness | 5.0572365 |
| Sum | 18824 |
| Variance | 95540.369 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 72 | 3 | 2.9% |
| 73 | 2 | 2.0% |
| 43 | 2 | 2.0% |
| 25 | 2 | 2.0% |
| 75 | 2 | 2.0% |
| 18 | 2 | 2.0% |
| 32 | 2 | 2.0% |
| 60 | 2 | 2.0% |
| 26 | 2 | 2.0% |
| 34 | 2 | 2.0% |
| Other values (74) | 81 |
| Value | Count | Frequency (%) |
| 9 | 1 | |
| 15 | 1 | |
| 17 | 1 | |
| 18 | 2 | |
| 21 | 1 | |
| 23 | 1 | |
| 24 | 1 | |
| 25 | 2 | |
| 26 | 2 | |
| 27 | 2 |
| Value | Count | Frequency (%) |
| 2547 | 1 | |
| 1115 | 1 | |
| 794 | 1 | |
| 792 | 1 | |
| 748 | 1 | |
| 655 | 1 | |
| 561 | 1 | |
| 536 | 1 | |
| 533 | 1 | |
| 532 | 1 |
Saves
Real number (ℝ)
High correlation
| Distinct | 84 |
|---|---|
| Distinct (%) | 82.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 156.54902 |
| Minimum | 22 |
|---|---|
| Maximum | 1095 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 22 |
|---|---|
| 5-th percentile | 35.05 |
| Q1 | 70.5 |
| median | 111 |
| Q3 | 173.5 |
| 95-th percentile | 466.6 |
| Maximum | 1095 |
| Range | 1073 |
| Interquartile range (IQR) | 103 |
Descriptive statistics
| Standard deviation | 157.77033 |
|---|---|
| Coefficient of variation (CV) | 1.0078015 |
| Kurtosis | 13.44031 |
| Mean | 156.54902 |
| Median Absolute Deviation (MAD) | 56 |
| Skewness | 3.1729853 |
| Sum | 15968 |
| Variance | 24891.478 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 135 | 3 | 2.9% |
| 144 | 3 | 2.9% |
| 74 | 2 | 2.0% |
| 98 | 2 | 2.0% |
| 49 | 2 | 2.0% |
| 40 | 2 | 2.0% |
| 111 | 2 | 2.0% |
| 99 | 2 | 2.0% |
| 90 | 2 | 2.0% |
| 34 | 2 | 2.0% |
| Other values (74) | 80 |
| Value | Count | Frequency (%) |
| 22 | 1 | |
| 28 | 1 | |
| 33 | 1 | |
| 34 | 2 | |
| 35 | 1 | |
| 36 | 1 | |
| 38 | 2 | |
| 40 | 2 | |
| 41 | 1 | |
| 42 | 2 |
| Value | Count | Frequency (%) |
| 1095 | 1 | |
| 668 | 1 | |
| 653 | 1 | |
| 573 | 1 | |
| 504 | 1 | |
| 469 | 1 | |
| 421 | 1 | |
| 393 | 1 | |
| 342 | 1 | |
| 318 | 1 |
Comments
Real number (ℝ)
Zeros
| Distinct | 15 |
|---|---|
| Distinct (%) | 14.7% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 6.3529412 |
| Minimum | 0 |
|---|---|
| Maximum | 19 |
| Zeros | 3 |
| Zeros (%) | 2.9% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 1.05 |
| Q1 | 4 |
| median | 6 |
| Q3 | 8 |
| 95-th percentile | 11 |
| Maximum | 19 |
| Range | 19 |
| Interquartile range (IQR) | 4 |
Descriptive statistics
| Standard deviation | 3.3080971 |
|---|---|
| Coefficient of variation (CV) | 0.52071898 |
| Kurtosis | 1.8884371 |
| Mean | 6.3529412 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | 0.76190046 |
| Sum | 648 |
| Variance | 10.943506 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=15)
| Value | Count | Frequency (%) |
| 6 | 16 | |
| 4 | 12 | |
| 7 | 12 | |
| 8 | 12 | |
| 5 | 10 | |
| 9 | 8 | |
| 3 | 7 | |
| 11 | 6 | 5.9% |
| 2 | 5 | 4.9% |
| 10 | 4 | 3.9% |
| Other values (5) | 10 |
| Value | Count | Frequency (%) |
| 0 | 3 | 2.9% |
| 1 | 3 | 2.9% |
| 2 | 5 | 4.9% |
| 3 | 7 | |
| 4 | 12 | |
| 5 | 10 | |
| 6 | 16 | |
| 7 | 12 | |
| 8 | 12 | |
| 9 | 8 |
| Value | Count | Frequency (%) |
| 19 | 1 | 1.0% |
| 17 | 1 | 1.0% |
| 13 | 2 | 2.0% |
| 11 | 6 | 5.9% |
| 10 | 4 | 3.9% |
| 9 | 8 | |
| 8 | 12 | |
| 7 | 12 | |
| 6 | 16 | |
| 5 | 10 |
Shares
Real number (ℝ)
High correlation Zeros
| Distinct | 28 |
|---|---|
| Distinct (%) | 27.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 9.3039216 |
| Minimum | 0 |
|---|---|
| Maximum | 75 |
| Zeros | 5 |
| Zeros (%) | 4.9% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 1 |
| Q1 | 3 |
| median | 6.5 |
| Q3 | 13 |
| 95-th percentile | 22.95 |
| Maximum | 75 |
| Range | 75 |
| Interquartile range (IQR) | 10 |
Descriptive statistics
| Standard deviation | 10.150149 |
|---|---|
| Coefficient of variation (CV) | 1.0909539 |
| Kurtosis | 17.459759 |
| Mean | 9.3039216 |
| Median Absolute Deviation (MAD) | 4.5 |
| Skewness | 3.3448765 |
| Sum | 949 |
| Variance | 103.02553 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=28)
| Value | Count | Frequency (%) |
| 3 | 11 | 10.8% |
| 1 | 10 | 9.8% |
| 4 | 7 | 6.9% |
| 5 | 7 | 6.9% |
| 6 | 6 | 5.9% |
| 15 | 6 | 5.9% |
| 8 | 5 | 4.9% |
| 7 | 5 | 4.9% |
| 0 | 5 | 4.9% |
| 2 | 5 | 4.9% |
| Other values (18) | 35 |
| Value | Count | Frequency (%) |
| 0 | 5 | |
| 1 | 10 | |
| 2 | 5 | |
| 3 | 11 | |
| 4 | 7 | |
| 5 | 7 | |
| 6 | 6 | |
| 7 | 5 | |
| 8 | 5 | |
| 9 | 3 | 2.9% |
| Value | Count | Frequency (%) |
| 75 | 1 | |
| 41 | 1 | |
| 38 | 1 | |
| 27 | 1 | |
| 26 | 1 | |
| 23 | 1 | |
| 22 | 2 | |
| 20 | 2 | |
| 19 | 1 | |
| 18 | 1 |
Likes
Real number (ℝ)
High correlation
| Distinct | 85 |
|---|---|
| Distinct (%) | 83.3% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 176.82353 |
| Minimum | 72 |
|---|---|
| Maximum | 549 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 72 |
|---|---|
| 5-th percentile | 82.15 |
| Q1 | 122 |
| median | 157.5 |
| Q3 | 208.75 |
| 95-th percentile | 327 |
| Maximum | 549 |
| Range | 477 |
| Interquartile range (IQR) | 86.75 |
Descriptive statistics
| Standard deviation | 85.151747 |
|---|---|
| Coefficient of variation (CV) | 0.48156344 |
| Kurtosis | 4.0043524 |
| Mean | 176.82353 |
| Median Absolute Deviation (MAD) | 41 |
| Skewness | 1.7191257 |
| Sum | 18036 |
| Variance | 7250.82 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 151 | 3 | 2.9% |
| 114 | 3 | 2.9% |
| 159 | 2 | 2.0% |
| 76 | 2 | 2.0% |
| 142 | 2 | 2.0% |
| 72 | 2 | 2.0% |
| 96 | 2 | 2.0% |
| 86 | 2 | 2.0% |
| 416 | 2 | 2.0% |
| 150 | 2 | 2.0% |
| Other values (75) | 80 |
| Value | Count | Frequency (%) |
| 72 | 2 | |
| 76 | 2 | |
| 81 | 1 | |
| 82 | 1 | |
| 85 | 1 | |
| 86 | 2 | |
| 91 | 1 | |
| 92 | 2 | |
| 94 | 1 | |
| 95 | 1 |
| Value | Count | Frequency (%) |
| 549 | 1 | |
| 443 | 1 | |
| 416 | 2 | |
| 373 | 1 | |
| 328 | 1 | |
| 308 | 1 | |
| 301 | 1 | |
| 297 | 1 | |
| 294 | 1 | |
| 275 | 1 |
Profile Visits
Real number (ℝ)
High correlation
| Distinct | 59 |
|---|---|
| Distinct (%) | 57.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 54.666667 |
| Minimum | 4 |
|---|---|
| Maximum | 611 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 4 |
|---|---|
| 5-th percentile | 8.05 |
| Q1 | 16 |
| median | 24 |
| Q3 | 45.75 |
| 95-th percentile | 234.2 |
| Maximum | 611 |
| Range | 607 |
| Interquartile range (IQR) | 29.75 |
Descriptive statistics
| Standard deviation | 93.169954 |
|---|---|
| Coefficient of variation (CV) | 1.7043284 |
| Kurtosis | 16.916099 |
| Mean | 54.666667 |
| Median Absolute Deviation (MAD) | 12 |
| Skewness | 3.8859065 |
| Sum | 5576 |
| Variance | 8680.6403 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 26 | 5 | 4.9% |
| 21 | 5 | 4.9% |
| 8 | 4 | 3.9% |
| 20 | 4 | 3.9% |
| 19 | 4 | 3.9% |
| 22 | 4 | 3.9% |
| 12 | 3 | 2.9% |
| 16 | 3 | 2.9% |
| 15 | 3 | 2.9% |
| 14 | 3 | 2.9% |
| Other values (49) | 64 |
| Value | Count | Frequency (%) |
| 4 | 1 | 1.0% |
| 7 | 1 | 1.0% |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 2 | |
| 11 | 3 | |
| 12 | 3 | |
| 13 | 2 | |
| 14 | 3 | |
| 15 | 3 |
| Value | Count | Frequency (%) |
| 611 | 1 | |
| 467 | 1 | |
| 347 | 1 | |
| 330 | 1 | |
| 306 | 1 | |
| 237 | 1 | |
| 181 | 1 | |
| 155 | 1 | |
| 148 | 1 | |
| 144 | 1 |
Follows
Real number (ℝ)
High correlation Zeros
| Distinct | 29 |
|---|---|
| Distinct (%) | 28.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 22.823529 |
| Minimum | 0 |
|---|---|
| Maximum | 260 |
| Zeros | 8 |
| Zeros (%) | 7.8% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 4 |
| median | 8 |
| Q3 | 18 |
| 95-th percentile | 95.9 |
| Maximum | 260 |
| Range | 260 |
| Interquartile range (IQR) | 14 |
Descriptive statistics
| Standard deviation | 43.685966 |
|---|---|
| Coefficient of variation (CV) | 1.9140758 |
| Kurtosis | 15.51751 |
| Mean | 22.823529 |
| Median Absolute Deviation (MAD) | 6 |
| Skewness | 3.7532633 |
| Sum | 2328 |
| Variance | 1908.4636 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=29)
| Value | Count | Frequency (%) |
| 4 | 13 | |
| 6 | 12 | |
| 2 | 12 | |
| 10 | 9 | 8.8% |
| 8 | 8 | 7.8% |
| 0 | 8 | 7.8% |
| 12 | 7 | 6.9% |
| 18 | 4 | 3.9% |
| 16 | 3 | 2.9% |
| 26 | 2 | 2.0% |
| Other values (19) | 24 |
| Value | Count | Frequency (%) |
| 0 | 8 | |
| 2 | 12 | |
| 4 | 13 | |
| 6 | 12 | |
| 8 | 8 | |
| 10 | 9 | |
| 12 | 7 | |
| 14 | 2 | 2.0% |
| 16 | 3 | 2.9% |
| 18 | 4 | 3.9% |
| Value | Count | Frequency (%) |
| 260 | 1 | |
| 228 | 1 | |
| 214 | 1 | |
| 100 | 2 | |
| 96 | 1 | |
| 94 | 2 | |
| 80 | 1 | |
| 74 | 1 | |
| 58 | 1 | |
| 46 | 1 |
Caption
Text
| Distinct | 90 |
|---|---|
| Distinct (%) | 88.2% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 26.3 KiB |
Length
| Max length | 784 |
|---|---|
| Median length | 216.5 |
| Mean length | 179.68627 |
| Min length | 44 |
Unique
| Unique | 79 ? |
|---|---|
| Unique (%) | 77.5% |
Sample
| 1st row | Here are some of the most important data visualizations that every Financial Data Analyst/Scientist should know. |
|---|---|
| 2nd row | Here are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects. |
| 3rd row | Learn how to train a machine learning model and giving inputs to your trained model to make predictions using Python. |
| 4th row | Heres how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence. |
| 5th row | Plotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python. |
| Value | Count | Frequency (%) |
| the | 163 | 5.2% |
| of | 125 | 4.0% |
| to | 113 | 3.6% |
| data | 95 | 3.0% |
| you | 94 | 3.0% |
| a | 81 | 2.6% |
| here | 74 | 2.3% |
| are | 67 | 2.1% |
| in | 60 | 1.9% |
| python | 50 | 1.6% |
| Other values (549) | 2228 |
Most occurring characters
| Value | Count | Frequency (%) |
| 3047 | ||
| e | 1864 | 10.2% |
| t | 1320 | 7.2% |
| a | 1288 | 7.0% |
| o | 1212 | 6.6% |
| n | 1125 | 6.1% |
| i | 1017 | 5.5% |
| s | 960 | 5.2% |
| r | 925 | 5.0% |
| h | 590 | 3.2% |
| Other values (63) | 4980 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 18328 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 3047 | ||
| e | 1864 | 10.2% |
| t | 1320 | 7.2% |
| a | 1288 | 7.0% |
| o | 1212 | 6.6% |
| n | 1125 | 6.1% |
| i | 1017 | 5.5% |
| s | 960 | 5.2% |
| r | 925 | 5.0% |
| h | 590 | 3.2% |
| Other values (63) | 4980 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 18328 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 3047 | ||
| e | 1864 | 10.2% |
| t | 1320 | 7.2% |
| a | 1288 | 7.0% |
| o | 1212 | 6.6% |
| n | 1125 | 6.1% |
| i | 1017 | 5.5% |
| s | 960 | 5.2% |
| r | 925 | 5.0% |
| h | 590 | 3.2% |
| Other values (63) | 4980 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 18328 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 3047 | ||
| e | 1864 | 10.2% |
| t | 1320 | 7.2% |
| a | 1288 | 7.0% |
| o | 1212 | 6.6% |
| n | 1125 | 6.1% |
| i | 1017 | 5.5% |
| s | 960 | 5.2% |
| r | 925 | 5.0% |
| h | 590 | 3.2% |
| Other values (63) | 4980 |
Hashtags
Text
| Distinct | 54 |
|---|---|
| Distinct (%) | 52.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 61.2 KiB |
Length
| Max length | 406 |
|---|---|
| Median length | 323 |
| Mean length | 261.23529 |
| Min length | 153 |
Unique
| Unique | 35 ? |
|---|---|
| Unique (%) | 34.3% |
Sample
| 1st row | #finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
|---|---|
| 2nd row | #healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
| 3rd row | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels |
| 4th row | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects |
| 5th row | #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal |
| Value | Count | Frequency (%) |
| amankharwal | 100 | 5.3% |
| thecleverprogrammer | 100 | 5.3% |
| python | 93 | 4.9% |
| pythonprogramming | 84 | 4.4% |
| pythonprojects | 82 | 4.3% |
| machinelearning | 81 | 4.3% |
| datascience | 79 | 4.2% |
| ai | 77 | 4.1% |
| artificialintelligence | 75 | 4.0% |
| data | 74 | 3.9% |
| Other values (154) | 1048 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 2827 | 10.6% |
| e | 2282 | 8.6% |
| n | 2092 | 7.9% |
| t | 1895 | 7.1% |
| # | 1892 | 7.1% |
| 1791 | 6.7% | |
| i | 1787 | 6.7% |
| r | 1505 | 5.6% |
| c | 1320 | 5.0% |
| o | 1224 | 4.6% |
| Other values (22) | 8031 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 26646 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| a | 2827 | 10.6% |
| e | 2282 | 8.6% |
| n | 2092 | 7.9% |
| t | 1895 | 7.1% |
| # | 1892 | 7.1% |
| 1791 | 6.7% | |
| i | 1787 | 6.7% |
| r | 1505 | 5.6% |
| c | 1320 | 5.0% |
| o | 1224 | 4.6% |
| Other values (22) | 8031 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 26646 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| a | 2827 | 10.6% |
| e | 2282 | 8.6% |
| n | 2092 | 7.9% |
| t | 1895 | 7.1% |
| # | 1892 | 7.1% |
| 1791 | 6.7% | |
| i | 1787 | 6.7% |
| r | 1505 | 5.6% |
| c | 1320 | 5.0% |
| o | 1224 | 4.6% |
| Other values (22) | 8031 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 26646 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| a | 2827 | 10.6% |
| e | 2282 | 8.6% |
| n | 2092 | 7.9% |
| t | 1895 | 7.1% |
| # | 1892 | 7.1% |
| 1791 | 6.7% | |
| i | 1787 | 6.7% |
| r | 1505 | 5.6% |
| c | 1320 | 5.0% |
| o | 1224 | 4.6% |
| Other values (22) | 8031 |
Engagement_Rate
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 6.3185371 |
| Minimum | 3.0526287 |
|---|---|
| Maximum | 13.033833 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 3.0526287 |
|---|---|
| 5-th percentile | 3.3722643 |
| Q1 | 4.7709739 |
| median | 6.2208744 |
| Q3 | 7.4704614 |
| 95-th percentile | 9.3570138 |
| Maximum | 13.033833 |
| Range | 9.9812038 |
| Interquartile range (IQR) | 2.6994875 |
Descriptive statistics
| Standard deviation | 2.0474364 |
|---|---|
| Coefficient of variation (CV) | 0.32403646 |
| Kurtosis | 0.53568084 |
| Mean | 6.3185371 |
| Median Absolute Deviation (MAD) | 1.395258 |
| Skewness | 0.69763743 |
| Sum | 644.49079 |
| Variance | 4.1919959 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 6.989795918 | 1 | 1.0% |
| 8.138672599 | 1 | 1.0% |
| 4.575976125 | 1 | 1.0% |
| 8.878091873 | 1 | 1.0% |
| 9.054805401 | 1 | 1.0% |
| 6.05046344 | 1 | 1.0% |
| 3.967951164 | 1 | 1.0% |
| 7.681445919 | 1 | 1.0% |
| 8.748999733 | 1 | 1.0% |
| 7.825030377 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 3.052628728 | 1 | |
| 3.116694854 | 1 | |
| 3.118546055 | 1 | |
| 3.265968964 | 1 | |
| 3.351735016 | 1 | |
| 3.364147527 | 1 | |
| 3.526483197 | 1 | |
| 3.643389456 | 1 | |
| 3.733098178 | 1 | |
| 3.762029746 | 1 |
| Value | Count | Frequency (%) |
| 13.0338325 | 1 | |
| 11.93578409 | 1 | |
| 11.35975725 | 1 | |
| 10.97739947 | 1 | |
| 10.06162141 | 1 | |
| 9.36468102 | 1 | |
| 9.21133703 | 1 | |
| 9.206877427 | 1 | |
| 9.12183055 | 1 | |
| 9.054805401 | 1 |
Home_ratio
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 4.9718113 |
| Minimum | 1.044493 |
|---|---|
| Maximum | 9.186551 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 1.044493 |
|---|---|
| 5-th percentile | 2.3352533 |
| Q1 | 3.7562499 |
| median | 4.8979119 |
| Q3 | 6.2270015 |
| 95-th percentile | 7.6200125 |
| Maximum | 9.186551 |
| Range | 8.142058 |
| Interquartile range (IQR) | 2.4707516 |
Descriptive statistics
| Standard deviation | 1.6746244 |
|---|---|
| Coefficient of variation (CV) | 0.3368238 |
| Kurtosis | -0.3703139 |
| Mean | 4.9718113 |
| Median Absolute Deviation (MAD) | 1.2089715 |
| Skewness | 0.082322193 |
| Sum | 507.12476 |
| Variance | 2.8043668 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 6.596938776 | 1 | 1.0% |
| 5.055617353 | 1 | 1.0% |
| 5.185277294 | 1 | 1.0% |
| 5.962897527 | 1 | 1.0% |
| 6.767275616 | 1 | 1.0% |
| 5.267765191 | 1 | 1.0% |
| 5.887066005 | 1 | 1.0% |
| 5.84863033 | 1 | 1.0% |
| 6.359029074 | 1 | 1.0% |
| 6.340218712 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 1.044492987 | 1 | |
| 1.38260035 | 1 | |
| 1.896458258 | 1 | |
| 1.957415017 | 1 | |
| 2.007052321 | 1 | |
| 2.323143249 | 1 | |
| 2.565343659 | 1 | |
| 2.671109701 | 1 | |
| 2.737376178 | 1 | |
| 2.825896763 | 1 |
| Value | Count | Frequency (%) |
| 9.186550976 | 1 | |
| 8.545216252 | 1 | |
| 8.498250875 | 1 | |
| 7.848258706 | 1 | |
| 7.726432532 | 1 | |
| 7.623549635 | 1 | |
| 7.552807831 | 1 | |
| 7.464440322 | 1 | |
| 7.45930644 | 1 | |
| 7.200180343 | 1 |
Explore_ratio
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1.2916152 |
| Minimum | 0 |
|---|---|
| Maximum | 6.994298 |
| Zeros | 1 |
| Zeros (%) | 1.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0.11599745 |
| Q1 | 0.40262753 |
| median | 0.75650866 |
| Q3 | 1.5149551 |
| 95-th percentile | 4.4416151 |
| Maximum | 6.994298 |
| Range | 6.994298 |
| Interquartile range (IQR) | 1.1123276 |
Descriptive statistics
| Standard deviation | 1.3936482 |
|---|---|
| Coefficient of variation (CV) | 1.0789964 |
| Kurtosis | 4.041122 |
| Mean | 1.2916152 |
| Median Absolute Deviation (MAD) | 0.47997165 |
| Skewness | 2.0200954 |
| Sum | 131.74475 |
| Variance | 1.9422553 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1.579081633 | 1 | 1.0% |
| 2.176492399 | 1 | 1.0% |
| 0 | 1 | 1.0% |
| 2.058303887 | 1 | 1.0% |
| 1.10802224 | 1 | 1.0% |
| 0.8470648816 | 1 | 1.0% |
| 1.27050744 | 1 | 1.0% |
| 1.4120305 | 1 | 1.0% |
| 0.6615097359 | 1 | 1.0% |
| 0.432563791 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 0 | 1 | |
| 0.07200720072 | 1 | |
| 0.07572172267 | 1 | |
| 0.08342602892 | 1 | |
| 0.08492569002 | 1 | |
| 0.1149425287 | 1 | |
| 0.1360410095 | 1 | |
| 0.1556824079 | 1 | |
| 0.1843817787 | 1 | |
| 0.19062339 | 1 |
| Value | Count | Frequency (%) |
| 6.994297973 | 1 | |
| 5.878894768 | 1 | |
| 5.326196666 | 1 | |
| 5.168176518 | 1 | |
| 5.09035056 | 1 | |
| 4.454075137 | 1 | |
| 4.204874019 | 1 | |
| 3.953934741 | 1 | |
| 3.906569343 | 1 | |
| 3.302730128 | 1 |
Hashtag_ratio
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 3.2260669 |
| Minimum | 0.31917336 |
|---|---|
| Maximum | 7.3963595 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0.31917336 |
|---|---|
| 5-th percentile | 0.65861551 |
| Q1 | 1.8708091 |
| median | 2.9796057 |
| Q3 | 4.3664069 |
| 95-th percentile | 6.160628 |
| Maximum | 7.3963595 |
| Range | 7.0771861 |
| Interquartile range (IQR) | 2.4955978 |
Descriptive statistics
| Standard deviation | 1.7726343 |
|---|---|
| Coefficient of variation (CV) | 0.54947226 |
| Kurtosis | -0.61951141 |
| Mean | 3.2260669 |
| Median Absolute Deviation (MAD) | 1.2355117 |
| Skewness | 0.40011406 |
| Sum | 329.05882 |
| Variance | 3.1422322 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 2.62244898 | 1 | 1.0% |
| 3.407489803 | 1 | 1.0% |
| 2.954488933 | 1 | 1.0% |
| 1.371466431 | 1 | 1.0% |
| 1.012708499 | 1 | 1.0% |
| 3.125643666 | 1 | 1.0% |
| 2.285387257 | 1 | 1.0% |
| 1.773510308 | 1 | 1.0% |
| 2.285942918 | 1 | 1.0% |
| 2.682867558 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 0.3191733639 | 1 | |
| 0.4193781634 | 1 | |
| 0.5074875208 | 1 | |
| 0.5113986445 | 1 | |
| 0.6507280244 | 1 | |
| 0.6585845347 | 1 | |
| 0.6592039801 | 1 | |
| 0.670015248 | 1 | |
| 0.6946526737 | 1 | |
| 0.9625324973 | 1 |
| Value | Count | Frequency (%) |
| 7.396359478 | 1 | |
| 7.357116175 | 1 | |
| 6.906690669 | 1 | |
| 6.366934848 | 1 | |
| 6.317960255 | 1 | |
| 6.161000152 | 1 | |
| 6.153557701 | 1 | |
| 6.134560457 | 1 | |
| 6.092271293 | 1 | |
| 6.045916034 | 1 |
Other_ratio
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 0.31524964 |
| Minimum | 0.032537961 |
|---|---|
| Maximum | 1.9084337 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 0.032537961 |
|---|---|
| 5-th percentile | 0.047760787 |
| Q1 | 0.11034841 |
| median | 0.15280197 |
| Q3 | 0.30540203 |
| 95-th percentile | 1.2432052 |
| Maximum | 1.9084337 |
| Range | 1.8758958 |
| Interquartile range (IQR) | 0.19505362 |
Descriptive statistics
| Standard deviation | 0.38489722 |
|---|---|
| Coefficient of variation (CV) | 1.2209284 |
| Kurtosis | 5.3611726 |
| Mean | 0.31524964 |
| Median Absolute Deviation (MAD) | 0.068110042 |
| Skewness | 2.338037 |
| Sum | 32.155463 |
| Variance | 0.14814587 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 0.1428571429 | 1 | 1.0% |
| 0.1446051168 | 1 | 1.0% |
| 1.32554091 | 1 | 1.0% |
| 0.1612190813 | 1 | 1.0% |
| 0.1469420175 | 1 | 1.0% |
| 0.1107106076 | 1 | 1.0% |
| 0.09538344143 | 1 | 1.0% |
| 0.16944366 | 1 | 1.0% |
| 0.1307015204 | 1 | 1.0% |
| 0.1117861482 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 0.03253796095 | 1 | |
| 0.03450345035 | 1 | |
| 0.04256712508 | 1 | |
| 0.042593469 | 1 | |
| 0.04530581425 | 1 | |
| 0.04753789578 | 1 | |
| 0.051995718 | 1 | |
| 0.05595709956 | 1 | |
| 0.05617977528 | 1 | |
| 0.05620437956 | 1 |
| Value | Count | Frequency (%) |
| 1.908433735 | 1 | |
| 1.80789401 | 1 | |
| 1.472724871 | 1 | |
| 1.403876375 | 1 | |
| 1.32554091 | 1 | |
| 1.247886371 | 1 | |
| 1.154263398 | 1 | |
| 1.027331411 | 1 | |
| 0.839944991 | 1 | |
| 0.8319467554 | 1 |
saves_to_likes
Real number (ℝ)
High correlation Unique
| Distinct | 102 |
|---|---|
| Distinct (%) | 100.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 7.8209784 |
| Minimum | 2.8947368 |
|---|---|
| Maximum | 20.365854 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 2.8947368 |
|---|---|
| 5-th percentile | 3.4593892 |
| Q1 | 5.3205882 |
| median | 7.2161466 |
| Q3 | 9.13653 |
| 95-th percentile | 15.330854 |
| Maximum | 20.365854 |
| Range | 17.471117 |
| Interquartile range (IQR) | 3.8159417 |
Descriptive statistics
| Standard deviation | 3.7317167 |
|---|---|
| Coefficient of variation (CV) | 0.47714192 |
| Kurtosis | 1.7388656 |
| Mean | 7.8209784 |
| Median Absolute Deviation (MAD) | 1.9234933 |
| Skewness | 1.2758481 |
| Sum | 797.7398 |
| Variance | 13.925709 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 6.049382716 | 1 | 1.0% |
| 8.660714286 | 1 | 1.0% |
| 3.129770992 | 1 | 1.0% |
| 8.075117371 | 1 | 1.0% |
| 7.804878049 | 1 | 1.0% |
| 5.138888889 | 1 | 1.0% |
| 2.894736842 | 1 | 1.0% |
| 10.88709677 | 1 | 1.0% |
| 9.748427673 | 1 | 1.0% |
| 6.387434555 | 1 | 1.0% |
| Other values (92) | 92 |
| Value | Count | Frequency (%) |
| 2.894736842 | 1 | |
| 3.129770992 | 1 | |
| 3.333333333 | 1 | |
| 3.401360544 | 1 | |
| 3.4375 | 1 | |
| 3.456790123 | 1 | |
| 3.50877193 | 1 | |
| 3.551401869 | 1 | |
| 3.578947368 | 1 | |
| 3.652173913 | 1 |
| Value | Count | Frequency (%) |
| 20.36585366 | 1 | |
| 19.94535519 | 1 | |
| 18.83534137 | 1 | |
| 16.36363636 | 1 | |
| 15.65055762 | 1 | |
| 15.36193029 | 1 | |
| 14.74040632 | 1 | |
| 14.49152542 | 1 | |
| 14.29090909 | 1 | |
| 13 | 1 |
Caption_Length
Real number (ℝ)
| Distinct | 76 |
|---|---|
| Distinct (%) | 74.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 179.68627 |
| Minimum | 44 |
|---|---|
| Maximum | 784 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 44 |
|---|---|
| 5-th percentile | 66.05 |
| Q1 | 87 |
| median | 131 |
| Q3 | 230.25 |
| 95-th percentile | 423.7 |
| Maximum | 784 |
| Range | 740 |
| Interquartile range (IQR) | 143.25 |
Descriptive statistics
| Standard deviation | 128.16681 |
|---|---|
| Coefficient of variation (CV) | 0.71328105 |
| Kurtosis | 4.7759639 |
| Mean | 179.68627 |
| Median Absolute Deviation (MAD) | 56 |
| Skewness | 1.8851932 |
| Sum | 18328 |
| Variance | 16426.732 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 79 | 4 | 3.9% |
| 87 | 4 | 3.9% |
| 91 | 3 | 2.9% |
| 187 | 3 | 2.9% |
| 81 | 2 | 2.0% |
| 181 | 2 | 2.0% |
| 106 | 2 | 2.0% |
| 233 | 2 | 2.0% |
| 104 | 2 | 2.0% |
| 85 | 2 | 2.0% |
| Other values (66) | 76 |
| Value | Count | Frequency (%) |
| 44 | 1 | |
| 53 | 1 | |
| 60 | 1 | |
| 63 | 1 | |
| 66 | 2 | |
| 67 | 1 | |
| 68 | 2 | |
| 69 | 1 | |
| 70 | 1 | |
| 77 | 1 |
| Value | Count | Frequency (%) |
| 784 | 1 | |
| 575 | 1 | |
| 504 | 1 | |
| 489 | 1 | |
| 447 | 1 | |
| 424 | 1 | |
| 418 | 1 | |
| 404 | 1 | |
| 367 | 1 | |
| 363 | 1 |
Top_source
Categorical
High correlation
| Distinct | 3 |
|---|---|
| Distinct (%) | 2.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 6.8 KiB |
| Home_ratio | |
|---|---|
| Hashtag_ratio | |
| Explore_ratio |
Length
| Max length | 13 |
|---|---|
| Median length | 10 |
| Mean length | 11.058824 |
| Min length | 10 |
Unique
| Unique | 0 ? |
|---|---|
| Unique (%) | 0.0% |
Sample
| 1st row | Home_ratio |
|---|---|
| 2nd row | Home_ratio |
| 3rd row | Home_ratio |
| 4th row | Home_ratio |
| 5th row | Home_ratio |
Common Values
| Value | Count | Frequency (%) |
| Home_ratio | 66 | |
| Hashtag_ratio | 27 | |
| Explore_ratio | 9 | 8.8% |
Length
Histogram of lengths of the category
Common Values (Plot)
| Value | Count | Frequency (%) |
| home_ratio | 66 | |
| hashtag_ratio | 27 | |
| explore_ratio | 9 | 8.8% |
Most occurring characters
| Value | Count | Frequency (%) |
| o | 177 | |
| a | 156 | |
| t | 129 | |
| r | 111 | |
| _ | 102 | |
| i | 102 | |
| H | 93 | |
| e | 75 | |
| m | 66 | 5.9% |
| s | 27 | 2.4% |
| Other values (6) | 90 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 1128 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| o | 177 | |
| a | 156 | |
| t | 129 | |
| r | 111 | |
| _ | 102 | |
| i | 102 | |
| H | 93 | |
| e | 75 | |
| m | 66 | 5.9% |
| s | 27 | 2.4% |
| Other values (6) | 90 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 1128 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| o | 177 | |
| a | 156 | |
| t | 129 | |
| r | 111 | |
| _ | 102 | |
| i | 102 | |
| H | 93 | |
| e | 75 | |
| m | 66 | 5.9% |
| s | 27 | 2.4% |
| Other values (6) | 90 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 1128 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| o | 177 | |
| a | 156 | |
| t | 129 | |
| r | 111 | |
| _ | 102 | |
| i | 102 | |
| H | 93 | |
| e | 75 | |
| m | 66 | 5.9% |
| s | 27 | 2.4% |
| Other values (6) | 90 |
Hashtag_count
Real number (ℝ)
| Distinct | 19 |
|---|---|
| Distinct (%) | 18.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 18.54902 |
| Minimum | 10 |
|---|---|
| Maximum | 30 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.6 KiB |
Quantile statistics
| Minimum | 10 |
|---|---|
| 5-th percentile | 11 |
| Q1 | 17 |
| median | 18 |
| Q3 | 20 |
| 95-th percentile | 29 |
| Maximum | 30 |
| Range | 20 |
| Interquartile range (IQR) | 3 |
Descriptive statistics
| Standard deviation | 4.7981586 |
|---|---|
| Coefficient of variation (CV) | 0.25867451 |
| Kurtosis | 0.57442284 |
| Mean | 18.54902 |
| Median Absolute Deviation (MAD) | 1 |
| Skewness | 0.59034408 |
| Sum | 1892 |
| Variance | 23.022326 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=19)
| Value | Count | Frequency (%) |
| 17 | 22 | |
| 18 | 17 | |
| 19 | 16 | |
| 11 | 9 | |
| 22 | 5 | 4.9% |
| 30 | 5 | 4.9% |
| 20 | 4 | 3.9% |
| 21 | 4 | 3.9% |
| 13 | 3 | 2.9% |
| 12 | 3 | 2.9% |
| Other values (9) | 14 |
| Value | Count | Frequency (%) |
| 10 | 2 | 2.0% |
| 11 | 9 | |
| 12 | 3 | 2.9% |
| 13 | 3 | 2.9% |
| 14 | 1 | 1.0% |
| 16 | 1 | 1.0% |
| 17 | 22 | |
| 18 | 17 | |
| 19 | 16 | |
| 20 | 4 | 3.9% |
| Value | Count | Frequency (%) |
| 30 | 5 | |
| 29 | 2 | 2.0% |
| 28 | 2 | 2.0% |
| 27 | 1 | 1.0% |
| 25 | 1 | 1.0% |
| 24 | 2 | 2.0% |
| 23 | 2 | 2.0% |
| 22 | 5 | |
| 21 | 4 | |
| 20 | 4 |
Interactions
Correlations
| Caption_Length | Comments | Engagement_Rate | Explore_ratio | Follows | From Explore | From Hashtags | From Home | From Other | Hashtag_count | Hashtag_ratio | Home_ratio | Impressions | Likes | Other_ratio | Post_ID | Profile Visits | Saves | Shares | Top_source | saves_to_likes | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Caption_Length | 1.000 | 0.059 | -0.247 | -0.123 | -0.001 | -0.118 | 0.115 | -0.243 | -0.188 | 0.140 | 0.257 | -0.117 | 0.000 | -0.131 | -0.162 | -0.028 | 0.106 | -0.229 | -0.343 | 0.000 | -0.229 |
| Comments | 0.059 | 1.000 | 0.192 | -0.087 | -0.012 | 0.042 | 0.212 | 0.348 | -0.068 | -0.027 | 0.202 | -0.060 | 0.279 | 0.357 | -0.155 | -0.257 | 0.098 | 0.228 | 0.153 | 0.200 | 0.032 |
| Engagement_Rate | -0.247 | 0.192 | 1.000 | 0.089 | -0.351 | 0.024 | -0.289 | 0.384 | -0.157 | 0.017 | -0.270 | 0.504 | -0.173 | 0.241 | -0.050 | -0.259 | -0.437 | 0.487 | 0.349 | 0.302 | 0.588 |
| Explore_ratio | -0.123 | -0.087 | 0.089 | 1.000 | 0.307 | 0.930 | -0.073 | 0.327 | 0.115 | -0.207 | -0.452 | -0.146 | 0.348 | 0.269 | -0.080 | 0.283 | 0.153 | 0.470 | 0.353 | 0.674 | 0.570 |
| Follows | -0.001 | -0.012 | -0.351 | 0.307 | 1.000 | 0.505 | 0.541 | 0.364 | 0.610 | -0.489 | 0.160 | -0.674 | 0.769 | 0.571 | 0.258 | 0.573 | 0.755 | 0.440 | 0.253 | 0.465 | 0.217 |
| From Explore | -0.118 | 0.042 | 0.024 | 0.930 | 0.505 | 1.000 | 0.201 | 0.486 | 0.262 | -0.315 | -0.227 | -0.370 | 0.614 | 0.510 | -0.031 | 0.386 | 0.341 | 0.624 | 0.459 | 0.634 | 0.593 |
| From Hashtags | 0.115 | 0.212 | -0.289 | -0.073 | 0.541 | 0.201 | 1.000 | 0.137 | 0.346 | -0.309 | 0.813 | -0.867 | 0.771 | 0.641 | 0.029 | 0.373 | 0.579 | 0.376 | 0.262 | 0.503 | 0.050 |
| From Home | -0.243 | 0.348 | 0.384 | 0.327 | 0.364 | 0.486 | 0.137 | 1.000 | 0.187 | -0.222 | -0.232 | 0.016 | 0.555 | 0.712 | -0.054 | 0.027 | 0.249 | 0.741 | 0.607 | 0.360 | 0.593 |
| From Other | -0.188 | -0.068 | -0.157 | 0.115 | 0.610 | 0.262 | 0.346 | 0.187 | 1.000 | -0.456 | 0.103 | -0.470 | 0.450 | 0.372 | 0.865 | 0.444 | 0.609 | 0.301 | 0.252 | 0.168 | 0.149 |
| Hashtag_count | 0.140 | -0.027 | 0.017 | -0.207 | -0.489 | -0.315 | -0.309 | -0.222 | -0.456 | 1.000 | -0.101 | 0.407 | -0.446 | -0.425 | -0.264 | -0.356 | -0.424 | -0.415 | -0.098 | 0.142 | -0.276 |
| Hashtag_ratio | 0.257 | 0.202 | -0.270 | -0.452 | 0.160 | -0.227 | 0.813 | -0.232 | 0.103 | -0.101 | 1.000 | -0.634 | 0.352 | 0.247 | 0.009 | 0.121 | 0.291 | -0.030 | -0.064 | 0.593 | -0.296 |
| Home_ratio | -0.117 | -0.060 | 0.504 | -0.146 | -0.674 | -0.370 | -0.867 | 0.016 | -0.470 | 0.407 | -0.634 | 1.000 | -0.784 | -0.508 | -0.138 | -0.536 | -0.660 | -0.284 | -0.176 | 0.563 | -0.014 |
| Impressions | 0.000 | 0.279 | -0.173 | 0.348 | 0.769 | 0.614 | 0.771 | 0.555 | 0.450 | -0.446 | 0.352 | -0.784 | 1.000 | 0.855 | 0.027 | 0.471 | 0.656 | 0.682 | 0.484 | 0.551 | 0.374 |
| Likes | -0.131 | 0.357 | 0.241 | 0.269 | 0.571 | 0.510 | 0.641 | 0.712 | 0.372 | -0.425 | 0.247 | -0.508 | 0.855 | 1.000 | 0.011 | 0.279 | 0.494 | 0.849 | 0.595 | 0.367 | 0.504 |
| Other_ratio | -0.162 | -0.155 | -0.050 | -0.080 | 0.258 | -0.031 | 0.029 | -0.054 | 0.865 | -0.264 | 0.009 | -0.138 | 0.027 | 0.011 | 1.000 | 0.201 | 0.317 | 0.001 | 0.068 | 0.000 | -0.022 |
| Post_ID | -0.028 | -0.257 | -0.259 | 0.283 | 0.573 | 0.386 | 0.373 | 0.027 | 0.444 | -0.356 | 0.121 | -0.536 | 0.471 | 0.279 | 0.201 | 1.000 | 0.374 | 0.309 | 0.146 | 0.567 | 0.269 |
| Profile Visits | 0.106 | 0.098 | -0.437 | 0.153 | 0.755 | 0.341 | 0.579 | 0.249 | 0.609 | -0.424 | 0.291 | -0.660 | 0.656 | 0.494 | 0.317 | 0.374 | 1.000 | 0.257 | 0.132 | 0.420 | -0.025 |
| Saves | -0.229 | 0.228 | 0.487 | 0.470 | 0.440 | 0.624 | 0.376 | 0.741 | 0.301 | -0.415 | -0.030 | -0.284 | 0.682 | 0.849 | 0.001 | 0.309 | 0.257 | 1.000 | 0.674 | 0.386 | 0.866 |
| Shares | -0.343 | 0.153 | 0.349 | 0.353 | 0.253 | 0.459 | 0.262 | 0.607 | 0.252 | -0.098 | -0.064 | -0.176 | 0.484 | 0.595 | 0.068 | 0.146 | 0.132 | 0.674 | 1.000 | 0.314 | 0.580 |
| Top_source | 0.000 | 0.200 | 0.302 | 0.674 | 0.465 | 0.634 | 0.503 | 0.360 | 0.168 | 0.142 | 0.593 | 0.563 | 0.551 | 0.367 | 0.000 | 0.567 | 0.420 | 0.386 | 0.314 | 1.000 | 0.369 |
| saves_to_likes | -0.229 | 0.032 | 0.588 | 0.570 | 0.217 | 0.593 | 0.050 | 0.593 | 0.149 | -0.276 | -0.296 | -0.014 | 0.374 | 0.504 | -0.022 | 0.269 | -0.025 | 0.866 | 0.580 | 0.369 | 1.000 |
Missing values
A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
Sample
| Post_ID | Impressions | From Home | From Hashtags | From Explore | From Other | Saves | Comments | Shares | Likes | Profile Visits | Follows | Caption | Hashtags | Engagement_Rate | Home_ratio | Explore_ratio | Hashtag_ratio | Other_ratio | saves_to_likes | Caption_Length | Top_source | Hashtag_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 3920 | 2586 | 1028 | 619 | 56 | 98 | 9 | 5 | 162 | 35 | 2 | Here are some of the most important data visualizations that every Financial Data Analyst/Scientist should know. | #finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer | 6.989796 | 6.596939 | 1.579082 | 2.622449 | 0.142857 | 6.049383 | 112 | Home_ratio | 22 |
| 1 | 2 | 5394 | 2727 | 1838 | 1174 | 78 | 194 | 7 | 14 | 224 | 48 | 10 | Here are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects. | #healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer | 8.138673 | 5.055617 | 2.176492 | 3.407490 | 0.144605 | 8.660714 | 187 | Home_ratio | 18 |
| 2 | 3 | 4021 | 2085 | 1188 | 0 | 533 | 41 | 11 | 1 | 131 | 62 | 12 | Learn how to train a machine learning model and giving inputs to your trained model to make predictions using Python. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels | 4.575976 | 5.185277 | 0.000000 | 2.954489 | 1.325541 | 3.129771 | 117 | Home_ratio | 18 |
| 3 | 4 | 4528 | 2700 | 621 | 932 | 73 | 172 | 10 | 7 | 213 | 23 | 8 | Heres how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects | 8.878092 | 5.962898 | 2.058304 | 1.371466 | 0.161219 | 8.075117 | 202 | Home_ratio | 11 |
| 4 | 5 | 2518 | 1704 | 255 | 279 | 37 | 96 | 5 | 4 | 123 | 8 | 0 | Plotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python. | #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal | 9.054805 | 6.767276 | 1.108022 | 1.012708 | 0.146942 | 7.804878 | 178 | Home_ratio | 29 |
| 5 | 6 | 3884 | 2046 | 1214 | 329 | 43 | 74 | 7 | 10 | 144 | 9 | 2 | Here are some of the most important soft skills that every data scientist should have. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #machinelearningalgorithms #ml #amankharwal #thecleverprogrammer #softskills | 6.050463 | 5.267765 | 0.847065 | 3.125644 | 0.110711 | 5.138889 | 86 | Home_ratio | 20 |
| 6 | 7 | 2621 | 1543 | 599 | 333 | 25 | 22 | 5 | 1 | 76 | 26 | 0 | Learn how to analyze a candlestick chart as a data scientist or a financial analyst. I hope this resource will help you to invest and analyze stock markets. | #stockmarket #investing #stocks #trading #money #investment #finance #forex #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #ai #candlestick #candlestickcharts | 3.967951 | 5.887066 | 1.270507 | 2.285387 | 0.095383 | 2.894737 | 156 | Home_ratio | 17 |
| 7 | 8 | 3541 | 2071 | 628 | 500 | 60 | 135 | 4 | 9 | 124 | 12 | 6 | Here are some of the best books that you can follow to learn Python from scratch. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects #pythonbooks #bookstagram | 7.681446 | 5.848630 | 1.412030 | 1.773510 | 0.169444 | 10.887097 | 81 | Home_ratio | 13 |
| 8 | 9 | 3749 | 2384 | 857 | 248 | 49 | 155 | 6 | 8 | 159 | 36 | 4 | Here are some of the best data analysis project ideas that you should try and show on your resume. These projects will help you to show your data analysis skills. | #dataanalytics #datascience #data #machinelearning #datavisualization #bigdata #artificialintelligence #datascientist #python #analytics #ai #dataanalysis #deeplearning #technology #programming #coding #dataanalyst #business #pythonprogramming #datamining #tech #businessintelligence #database #computerscience #statistics #powerbi #dataanalysisprojects #businessanalytics #thecleverprogrammer #amankharwal | 8.749000 | 6.359029 | 0.661510 | 2.285943 | 0.130702 | 9.748428 | 162 | Home_ratio | 30 |
| 9 | 10 | 4115 | 2609 | 1104 | 178 | 46 | 122 | 6 | 3 | 191 | 31 | 6 | Here are two best ways to count the number of letters in a string using Python. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects | 7.825030 | 6.340219 | 0.432564 | 2.682868 | 0.111786 | 6.387435 | 79 | Home_ratio | 11 |
| Post_ID | Impressions | From Home | From Hashtags | From Explore | From Other | Saves | Comments | Shares | Likes | Profile Visits | Follows | Caption | Hashtags | Engagement_Rate | Home_ratio | Explore_ratio | Hashtag_ratio | Other_ratio | saves_to_likes | Caption_Length | Top_source | Hashtag_count | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | 93 | 17713 | 2449 | 2141 | 12389 | 561 | 504 | 3 | 23 | 308 | 70 | 96 | Here are some of the best resources to learn SQL for data science. | #sql #mysql #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 4.730989 | 1.382600 | 6.994298 | 1.208717 | 0.316717 | 16.363636 | 66 | Explore_ratio | 19 |
| 110 | 94 | 5563 | 3813 | 362 | 1135 | 76 | 149 | 5 | 8 | 163 | 22 | 20 | Here are the best Python libraries for data visualization that you should learn for data science. | #datavisualization #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 5.842171 | 6.854215 | 2.040266 | 0.650728 | 0.136617 | 9.141104 | 97 | Home_ratio | 18 |
| 111 | 95 | 4842 | 1658 | 694 | 2036 | 310 | 55 | 6 | 4 | 86 | 46 | 30 | Learn how to create an interactive language translator using the Python programming language. | #python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer #nlp #naturallanguageprocessing | 3.118546 | 3.424205 | 4.204874 | 1.433292 | 0.640231 | 6.395349 | 93 | Explore_ratio | 14 |
| 112 | 96 | 11149 | 4439 | 747 | 5762 | 53 | 273 | 4 | 13 | 210 | 61 | 58 | Python is one of the best programming languages for numerical calculations. So you should know how to calculate mean, median and mode using Python without using any built-in Python library or module. Heres how to calculate mean, median, and mode using Python. | #python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer | 4.484707 | 3.981523 | 5.168177 | 0.670015 | 0.047538 | 13.000000 | 260 | Explore_ratio | 12 |
| 113 | 97 | 10206 | 2371 | 1624 | 6000 | 117 | 182 | 10 | 17 | 172 | 237 | 100 | Practice these 90+ Data Science Projects For Beginners Solved & Explained using Python. Find all these projects from the link in bio. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 3.733098 | 2.323143 | 5.878895 | 1.591221 | 0.114638 | 10.581395 | 133 | Explore_ratio | 17 |
| 114 | 98 | 13700 | 5185 | 3041 | 5352 | 77 | 573 | 2 | 38 | 373 | 73 | 80 | Here are some of the best data science certifications that you can choose from in 2022. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 7.197080 | 3.784672 | 3.906569 | 2.219708 | 0.056204 | 15.361930 | 87 | Explore_ratio | 17 |
| 115 | 99 | 5731 | 1923 | 1368 | 2266 | 65 | 135 | 4 | 1 | 148 | 20 | 18 | Clustering is a machine learning technique used to classify data points, charaterized by some specific features into groups. It is an unsupervised machine learning method where the data we deal with is not labelled. Here are some of the best Machine Learning project ideas on Clustering that you should try. | #machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering | 5.025301 | 3.355435 | 3.953935 | 2.387018 | 0.113418 | 9.121622 | 307 | Explore_ratio | 18 |
| 116 | 100 | 4139 | 1133 | 1538 | 1367 | 33 | 36 | 0 | 1 | 92 | 34 | 10 | Clustering music genres is a task of grouping music based on the similarities in their audio characteristics. Here you will learn how to do clustering analysis of music genres with Machine Learning using Python. | #machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering | 3.116695 | 2.737376 | 3.302730 | 3.715873 | 0.079729 | 3.913043 | 211 | Hashtag_ratio | 18 |
| 117 | 101 | 32695 | 11815 | 3147 | 17414 | 170 | 1095 | 2 | 75 | 549 | 148 | 214 | Here are some of the best data science certifications that you can choose from in 2022. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 5.263802 | 3.613702 | 5.326197 | 0.962532 | 0.051996 | 19.945355 | 87 | Explore_ratio | 17 |
| 118 | 102 | 36919 | 13473 | 4176 | 16444 | 2547 | 653 | 5 | 26 | 443 | 611 | 228 | 175 Python Projects with Source Code solved and explained for free: Link in Bio | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects | 3.052629 | 3.649340 | 4.454075 | 1.131125 | 0.689889 | 14.740406 | 79 | Explore_ratio | 11 |